The Medical Schools Council, working with HDR UK, has recommended that UK medical schools prepare students in health informatics, AI and data science, spanning data governance and the ethical, professional, legal and regulatory dimensions, which is the institutional way of saying something concrete: the FY1 of the late 2020s starts work in a digitally saturated NHS and is expected to arrive able to handle it. This article translates the recommendation into the currency that matters to a final-year student, realistic FY1 scenarios, and organises the whole around one distinction the recommendations imply but students must internalise: learning to use an AI tool is easy and perishable; learning to supervise its work is hard and career-long.
Four FY1 scenarios, worked
Scenario one, the ambient scribe: the trust runs AI-assisted documentation, and the regulatory position is settled, transcription-and-summary tools for clinician review are not medical devices, and the clinician remains responsible for reviewing outputs before they enter the record. The FY1 skill is therefore review discipline: reading the generated note as a draft to be corrected, not a fact to be countersigned, and knowing that the signature makes it yours. Scenario two, the decision-support answer: a colleague quotes an AI tool's management suggestion on the post-take round. The skill is provenance reflex, which tool, grounded in what, dated when, for which country, and the confidence to say "let's open the guideline", which grounded tools make a ten-second act. Scenario three, the patient who used AI: a patient arrives with a chatbot-generated self-diagnosis. The skill is communication without contempt, taking the underlying concern seriously, correcting the specifics against proper sources, and explaining your own tool use if asked, the transparency habit the era requires. Scenario four, the data request: an audit needs patient data extracted and someone suggests a convenient consumer tool. The skill is governance reflex, identifiable data stays inside governed systems, full stop, and the FY1 who says so out loud is practising the accountability the recommendations name.
What to build in final year
Five capabilities, each buildable before graduation. The verification workflow, run until it is reflex, because FY1 time pressure will not permit learning it live. The regulatory map at working depth: what device status does and does not mean, where the GMC's responsibility line sits, enough to locate any new tool in seconds. The never-paste rules with their reasoning, rehearsed against placement-realistic cases. Unaided clinical retrieval at UKMLA standard, since the examination's unassisted condition is also the ward's at 3am, and AI-supported study must keep finishing there. And the supervision skill itself, practised deliberately: take AI outputs, find the planted or natural errors, document the check, because this, not prompt fluency, is what the recommendations mean by preparing graduates for AI, and it is the capability every subsequent scenario reuses.
UKMLA preparation as digital-practice preparation
The convenient alignment worth exploiting: the habits that pass the UKMLA are the habits the digitally enabled foundation job rewards. Blueprint-mapped retrieval builds the unaided knowledge that makes supervision possible, you cannot check what you cannot independently doubt; the updated content map's emphasis on managing uncertainty and patient-centred care mirrors exactly the judgement AI cannot supply; and the discipline of finishing every study arc without the tool is a rehearsal of the ward's baseline condition. A final year spent this way graduates twice, once past the examination and once into the era, with the same hours. The recommendations, read from a student's chair, are not an additional subject; they are a description of what competent already means.
Frequently asked questions
Will foundation programmes teach this if my school didn't?
Unevenly, which is the honest answer and the argument for self-building the five capabilities; the scenarios above arrive on rota schedules, not curriculum schedules.
Are there tools an FY1 should personally avoid?
Categories rather than brands: anything requiring identifiable data outside governed systems, anything whose sources cannot be opened, and anything whose intended use excludes clinical decision support while being used for it.
How does this connect to portfolio and appraisal later?
Directly: documented verification habits, disclosed tool use and reflective notes on AI-involved cases are portfolio-ready evidence of exactly the capability the profession is formalising, cheap to collect from day one.
What about tools the trust provides that I distrust?
Use through governance, not around it: raise specific concerns with supervisors or the clinical-safety route, document your own verification where outputs touch care, and remember that provided is not the same as infallible, which the review-discipline habit already assumes.
Do these scenarios differ across the four UK nations?
In machinery, marginally; in principle, no: device regulation and GMC accountability are UK-wide, while records systems and local governance vary by nation and trust, which is the relocalisation layer every rotation already demands.